Zooplankton avoidance behaviour as a response to point sources of hydrocarbon-contaminated water
Bibliographic record
Abstract
Hydrocarbon contamination is a pernicious threat for marine ecosystems as non-lethal effects on the plankton propagate through the food chain and accumulate in the tissues of top predators, ultimately putting human health at risk. The swimming behaviour of the calanoid copepods Eurytemora affinis and Temora longicornis was investigated in relation to point-source contamination by five different-sized patches of the water-soluble fraction of diesel oil diluted at 1 : 100, 1 : 1000 and 1 : 10 000 in estuarine and coastal waters. Both species consistently showed avoidance of the contaminated patches, irrespective of their size and concentration. Specifically, E. affinis exhibited similar sensory abilities irrespective of contaminant concentrations. In contrast, T. longicornis more efficiently identified high-density contaminated patches than low-density ones, and exhibited a negative exponential density dependence of its sensing abilities to the intensity of the chemical cues. Although the conclusions from this experiment need to be generalised to a variety of hydrocarbon contaminants, the present work indicates that zooplankton organisms have the potential to avoid hydrocarbon-contaminated waters, and also suggests that zooplankton swimming behaviour could potentially be used as an endpoint for a toxicity bioassay to assess the presence of toxic chemicals in estuarine and coastal waters.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".